EM Algorithm

The Expectation-Maximization (EM) algorithm is an iterative method for finding maximum likelihood estimates in statistical models with latent variables, aiming to uncover hidden patterns within data. Current research focuses on extending EM's applicability to diverse models, including Gaussian mixtures, Markov chains, and even deep neural networks, often incorporating modifications like regularization or federated learning to address challenges such as high dimensionality, limited data, and distributed computation. These advancements improve the algorithm's efficiency, robustness, and accuracy across various applications, from clustering and classification to video object segmentation and natural language processing.